Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
Big Data and Transport Understanding and assessing optionsLudovic Privat
Massive amounts of digital data are being generated from a variety of sources including sensors, devices and online activities, with estimates that the total "digital universe" will grow to 44 zettabytes by 2020. This data holds potential value when combined from different sources to provide new insights but also risks around privacy if used without appropriate protections. Transport authorities will need to evaluate how new and existing data sources can help improve operations, planning and safety while ensuring regulations keep pace with changing data practices.
The document provides information about the SmartLab research group at the University of Genoa in Italy. It discusses SmartLab's work in areas like real-time analytics for fuel prediction and skid prediction in racing cars. It also mentions past projects involving traffic forecasting and bus arrival time prediction. The document outlines SmartLab's computing resources and plans to expand its IBM cluster. It discusses potential future work in areas like process mining, condition-based maintenance using NoSQL databases, and advanced data analytics.
Big data Europe the transport pilot in Thessaloniki - Josep Maria SalanovaBigData_Europe
The document discusses mobile sensor data collection in Thessaloniki, Greece for transportation analysis. It describes using stationary Bluetooth sensors to track device IDs for travel time estimation and origin-destination analysis. It also uses floating car data from taxis and buses for traffic status and mobility pattern analysis. The data is processed using map matching and time series forecasting algorithms to classify current traffic states and predict future conditions. Websites and data portals for accessing the collected transportation data are also listed.
Yinhai Wang - Smart Transportation: Research under Smart Cities Context - GCS16KC Digital Drive
This document discusses challenges and opportunities in smart transportation research under smart cities. It outlines how transportation is a major issue impacting environment, safety, and public health. Smart cities and transportation big data can help address key issues of efficiency, sustainability and safety through data analytics. The document presents examples of extracting transportation data from mobile networks and DRIVE Net, a system for data sharing, visualization, and analysis to support e-science investigations in transportation. Research needs include developing methods to utilize spatial and temporal big data to support analysis and decision making in transportation.
SC4 Workshop 1: Roberto Baldessari: The use of big data for public transport ...BigData_Europe
This document discusses how public transportation agencies can use big data to improve performance. It provides examples of how transportation agencies can use automated vehicle location (AVL) data from buses to identify excess waiting times, busy routes, and bottlenecks. This data can then be used to optimize schedules and reduce excess waiting times. The document also discusses how AVL data combined with automatic passenger counter (APC) data can be used to analyze bus load profiles and driving patterns to find opportunities for fuel savings and safety improvements. Finally, the document presents a vision of integrating various data sources like video, sensors and customer apps to further optimize public transportation systems.
Smarter Cites Challenge 05202016 LG FinalLew Gaskell
This document discusses how smarter transportation systems can help cities address challenges of population growth, increasing traffic congestion, and environmental concerns. It describes how intelligent transportation systems using IoT, connected vehicles, and advanced analytics can help optimize public transit routes and schedules to reduce congestion. Cities can leverage these technologies to gain insights into mobility patterns, anticipate transportation demands, and improve traffic flow to support sustainable growth while minimizing environmental impacts.
Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
Big Data and Transport Understanding and assessing optionsLudovic Privat
Massive amounts of digital data are being generated from a variety of sources including sensors, devices and online activities, with estimates that the total "digital universe" will grow to 44 zettabytes by 2020. This data holds potential value when combined from different sources to provide new insights but also risks around privacy if used without appropriate protections. Transport authorities will need to evaluate how new and existing data sources can help improve operations, planning and safety while ensuring regulations keep pace with changing data practices.
The document provides information about the SmartLab research group at the University of Genoa in Italy. It discusses SmartLab's work in areas like real-time analytics for fuel prediction and skid prediction in racing cars. It also mentions past projects involving traffic forecasting and bus arrival time prediction. The document outlines SmartLab's computing resources and plans to expand its IBM cluster. It discusses potential future work in areas like process mining, condition-based maintenance using NoSQL databases, and advanced data analytics.
Big data Europe the transport pilot in Thessaloniki - Josep Maria SalanovaBigData_Europe
The document discusses mobile sensor data collection in Thessaloniki, Greece for transportation analysis. It describes using stationary Bluetooth sensors to track device IDs for travel time estimation and origin-destination analysis. It also uses floating car data from taxis and buses for traffic status and mobility pattern analysis. The data is processed using map matching and time series forecasting algorithms to classify current traffic states and predict future conditions. Websites and data portals for accessing the collected transportation data are also listed.
Yinhai Wang - Smart Transportation: Research under Smart Cities Context - GCS16KC Digital Drive
This document discusses challenges and opportunities in smart transportation research under smart cities. It outlines how transportation is a major issue impacting environment, safety, and public health. Smart cities and transportation big data can help address key issues of efficiency, sustainability and safety through data analytics. The document presents examples of extracting transportation data from mobile networks and DRIVE Net, a system for data sharing, visualization, and analysis to support e-science investigations in transportation. Research needs include developing methods to utilize spatial and temporal big data to support analysis and decision making in transportation.
SC4 Workshop 1: Roberto Baldessari: The use of big data for public transport ...BigData_Europe
This document discusses how public transportation agencies can use big data to improve performance. It provides examples of how transportation agencies can use automated vehicle location (AVL) data from buses to identify excess waiting times, busy routes, and bottlenecks. This data can then be used to optimize schedules and reduce excess waiting times. The document also discusses how AVL data combined with automatic passenger counter (APC) data can be used to analyze bus load profiles and driving patterns to find opportunities for fuel savings and safety improvements. Finally, the document presents a vision of integrating various data sources like video, sensors and customer apps to further optimize public transportation systems.
Smarter Cites Challenge 05202016 LG FinalLew Gaskell
This document discusses how smarter transportation systems can help cities address challenges of population growth, increasing traffic congestion, and environmental concerns. It describes how intelligent transportation systems using IoT, connected vehicles, and advanced analytics can help optimize public transit routes and schedules to reduce congestion. Cities can leverage these technologies to gain insights into mobility patterns, anticipate transportation demands, and improve traffic flow to support sustainable growth while minimizing environmental impacts.
SC4 Workshop 1: Evangelos Mitsakis: Big data Sources for/from Intelligent Roa...BigData_Europe
This document discusses big data sources for intelligent road transport. It explains that even small datasets from individual vehicles can grow very large in aggregate when many vehicles are transmitting GPS data. Transportation agencies are now collecting petabytes of data on traffic patterns, public transit use, and vehicle locations using sensors, vehicle fleets, and smartphones. This big data is helping to optimize traffic management, asset maintenance, and traveler information services. Researchers are also able to conduct more accurate studies without relying on samples by analyzing vast amounts of real-world transportation data. The document provides examples of big data collection and use for traffic, public transit, and smart cities in Greece.
Next Generation Intelligent Transportation: Solutions for Smart CitiesUGPTI
This March 1 seminar presentation provided an overview of key technology trends that are steadily transforming our transportation system. Bridgelall provided a sample of research needs that exposed the complexities and interdependencies between transportation supply, transportation demand, performance measures, and policy making.
Technical article reproduced with permission from Transportation Professional - the magazine of the Chartered Institution of Highways & Transportation
www.ciht.org.uk/en/knowledge/publications/transportation-professional/index.cfm
This document discusses smart cities in Romania. It provides definitions of smart cities from the European Commission and British Standards Institute. It then outlines some of the key challenges facing cities, such as increasing populations, economic growth, and environmental issues. It discusses Romania's Authority for Digitalization and some of its current projects to improve digital identification and public sector interoperability. Finally, it notes barriers to digitalization in Romania's public and private sectors, and provides an overview of the Romanian Association for Smart Cities and some statistics about Romania's digital performance based on the DESI index.
Smart Road Technology for Traffic Management and ITS Infrastructure Assessmen...IJAEMSJORNAL
This technical work describe infrastructure requirement and the working principles and procedures involved in operation of a Smart Road. A Smart Road is similar to a conventional highway but the difference is, it is equipped with the electronic gadgets required to capture static and dynamic physical entities occupied on the road at a given time and location. Nowadays traffic safety and highway congestion has become a serious concern to the Authorities and required to be managed them within the available resources. Also it is not possible to increase the capacity of highway infrastructure to compete with increase in traffic. In cities on highway system, large amount of traffic data being generated and an integrated approach is required for the efficient management transportation system. Smart Road is an innovative approach wherein Information Communication Technologies (ICT) is merged with traditional infrastructure and integrated with digital technologies. Critical examination of literature review reveals that many technologies are available for data capturing and management. Notable among them are by using ultrasonic sensors, light sensors, motion sensors, camera and IOT devices. The data collected by the devices would be managed through cloud computing and big data analytic methods. To assess the current traffic situation spot speeds and traffic volumes are captured for peak and non-peak on the Express Highway and from the data captured 85th percentile speed and LoS are estimated. Smart road technology is discussed for transportation system management. And IT infrastructure requirement for capturing traffic related data demonstrated for the selected road in Muscat.
Read more at :https://www.alliedmarketresearch.com/smart-transportation-market
Smart Transportation Market Report explains different service offerings of the industry.The market involves all applications and hardware that comprise an intelligent system that monitors and controls the movement of traffic or transport. Simply, it implies toward an integrated machine-controlled environment, which can automatically track, trace, register, and navigate different modes of transport. The market offers innovative software applications to manage ticketing, parking, and traffic regulating facilities. Technological innovations in the field of electronic sensors and big data management introduces fresh services in the smart transportation market.
1) The presentation discusses how disruptive technologies will impact urban mobility and deliver innovative solutions to support smart cities.
2) It outlines challenges like increasing traffic, costs of congestion, and emissions, and opportunities from technologies like mobile internet, IoT, cloud computing and autonomous vehicles.
3) The presentation argues that integrating data from networked infrastructure can optimize operations through predictive analytics and transform conventional approaches to mobility.
The document discusses Seoul's Connected Urban Development program and its initiatives to promote smart transportation, including Smart Transportation Pricing, Smart Work Centers, Personal Travel Assistants, and Connected Buses. The goals are to reduce traffic congestion, encourage public transit and more sustainable travel options, and improve citizens' transportation experiences through real-time information and integrated payment systems. Key features for each initiative and their anticipated impacts on traffic, the environment, and mobility patterns are described.
Traffic congestion has become one of the largest annoyances to countries like the United States and China when it comes to economic competitiveness, livability, safety, and long-term environmental sustainability. Congestion on freeways and roads is a major component of and contributor to delay. Emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), and connected vehicles provide opportunities to mitigate traffic congestion.
As one of the largest technology providers in China, Asiainfo Data is leading a city-scale Smart Transportation project in City of Wuxi, one of the national centers for IoT innovation in China. In this talk, we would like to share our real-world experiences in using AI and big data technologies to implement cutting-edge technologies transformation of Wuxi’s transportation systems. In this project, we collaborate with China Telecom, the Traffic Management Research Institute, smart vehicle makers, and many influential researchers on traffic management worldwide. We will showcase scenarios on public transportation prioritization, car-road coordination, and region-wide traffic signal optimization by utilizing AI modeling and the most advanced IoT technologies.
Speaker
Jian Chang, AsiaInfo, Chief Technology Officer
Transport for Cairo is an ambitious project that seeks to map all formal and informal transportation in Cairo digitally, and release all data openly.
This Presentation is outdated as of April 2016.
Based on a future vision of a multi-modal, end-to-end UK mobility system please describe your view of the role of Customer Experience in achieving this vision and where experience from other industry sectors can be used to add value
Cities are facing increasing mobility problems as populations grow. Public transportation systems generate large amounts of data from various sources, but there is a gap between the available data and the knowledge that can be extracted. The document discusses challenges around data integration, collaboration, and knowledge extraction in order to improve public transportation planning, operations, and passenger information systems through solutions like optimization algorithms, real-time tracking and alerts, and multimodal route planners. Political commitment is needed to fully leverage the available data.
The role of open data in driving sustainable mobility in nine smart citiesPiyush Yadav
The work was presented in European Conference on Information Systems (ECIS 2017) , at Guimaraes Portugal. The work presents a comprehensive survey results on open data focused in mobility domain in nine smart cities like Barcelona, Dublin, NewYork etc.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Km4City: how to make smart and resilient your city, beginner documentPaolo Nesi
The Km4City platform aggregates data from multiple city domains to provide integrated data services and generate suggestions to engage citizens and support social innovation. It enables a wide range of applications while keeping city services and status under control through a flexible dashboard. The platform assesses and improves city resilience, safety and security by analyzing city usage at multiple levels. It accelerates the implementation of business and service applications by enabling integrated city services through third party portals.
Innovative Approaches for Smart City Development
ดิจิทัลไทยแลนด์ 2016: วิธีการใหม่ การพัฒนาเมืองอัจฉิริยา Trends and case studies from Germany, UK, and rest of Europe. Focus on how to get started and medium sized cities. Presented at Digital Thailand Days on 27 May 2016. www.facebook.com/events/1088455231202211
www.facebook.com/digitalthailandday/
www.digitalthailand.in.th/ #digitalthailand #digitalthailand2016
국내 중소기업,벤처기업, 스타트업의 해외 진출 영업 및 마케팅,해외 기업과 공동연구, 글로벌 VC로부터 투자유치에 대한 프로젝트 세미나중 해외 기업과 공동연구에 관하여 이스라엘의 해외 기업 공동연구 부분에 대한 발표내용만 정리했습니다. 해외진출 마케팅과 글로벌 VC로부터 투자유치에 대한 내용은 프로젝트 기밀사항이라 제외합니다.
SC4 Workshop 1: Evangelos Mitsakis: Big data Sources for/from Intelligent Roa...BigData_Europe
This document discusses big data sources for intelligent road transport. It explains that even small datasets from individual vehicles can grow very large in aggregate when many vehicles are transmitting GPS data. Transportation agencies are now collecting petabytes of data on traffic patterns, public transit use, and vehicle locations using sensors, vehicle fleets, and smartphones. This big data is helping to optimize traffic management, asset maintenance, and traveler information services. Researchers are also able to conduct more accurate studies without relying on samples by analyzing vast amounts of real-world transportation data. The document provides examples of big data collection and use for traffic, public transit, and smart cities in Greece.
Next Generation Intelligent Transportation: Solutions for Smart CitiesUGPTI
This March 1 seminar presentation provided an overview of key technology trends that are steadily transforming our transportation system. Bridgelall provided a sample of research needs that exposed the complexities and interdependencies between transportation supply, transportation demand, performance measures, and policy making.
Technical article reproduced with permission from Transportation Professional - the magazine of the Chartered Institution of Highways & Transportation
www.ciht.org.uk/en/knowledge/publications/transportation-professional/index.cfm
This document discusses smart cities in Romania. It provides definitions of smart cities from the European Commission and British Standards Institute. It then outlines some of the key challenges facing cities, such as increasing populations, economic growth, and environmental issues. It discusses Romania's Authority for Digitalization and some of its current projects to improve digital identification and public sector interoperability. Finally, it notes barriers to digitalization in Romania's public and private sectors, and provides an overview of the Romanian Association for Smart Cities and some statistics about Romania's digital performance based on the DESI index.
Smart Road Technology for Traffic Management and ITS Infrastructure Assessmen...IJAEMSJORNAL
This technical work describe infrastructure requirement and the working principles and procedures involved in operation of a Smart Road. A Smart Road is similar to a conventional highway but the difference is, it is equipped with the electronic gadgets required to capture static and dynamic physical entities occupied on the road at a given time and location. Nowadays traffic safety and highway congestion has become a serious concern to the Authorities and required to be managed them within the available resources. Also it is not possible to increase the capacity of highway infrastructure to compete with increase in traffic. In cities on highway system, large amount of traffic data being generated and an integrated approach is required for the efficient management transportation system. Smart Road is an innovative approach wherein Information Communication Technologies (ICT) is merged with traditional infrastructure and integrated with digital technologies. Critical examination of literature review reveals that many technologies are available for data capturing and management. Notable among them are by using ultrasonic sensors, light sensors, motion sensors, camera and IOT devices. The data collected by the devices would be managed through cloud computing and big data analytic methods. To assess the current traffic situation spot speeds and traffic volumes are captured for peak and non-peak on the Express Highway and from the data captured 85th percentile speed and LoS are estimated. Smart road technology is discussed for transportation system management. And IT infrastructure requirement for capturing traffic related data demonstrated for the selected road in Muscat.
Read more at :https://www.alliedmarketresearch.com/smart-transportation-market
Smart Transportation Market Report explains different service offerings of the industry.The market involves all applications and hardware that comprise an intelligent system that monitors and controls the movement of traffic or transport. Simply, it implies toward an integrated machine-controlled environment, which can automatically track, trace, register, and navigate different modes of transport. The market offers innovative software applications to manage ticketing, parking, and traffic regulating facilities. Technological innovations in the field of electronic sensors and big data management introduces fresh services in the smart transportation market.
1) The presentation discusses how disruptive technologies will impact urban mobility and deliver innovative solutions to support smart cities.
2) It outlines challenges like increasing traffic, costs of congestion, and emissions, and opportunities from technologies like mobile internet, IoT, cloud computing and autonomous vehicles.
3) The presentation argues that integrating data from networked infrastructure can optimize operations through predictive analytics and transform conventional approaches to mobility.
The document discusses Seoul's Connected Urban Development program and its initiatives to promote smart transportation, including Smart Transportation Pricing, Smart Work Centers, Personal Travel Assistants, and Connected Buses. The goals are to reduce traffic congestion, encourage public transit and more sustainable travel options, and improve citizens' transportation experiences through real-time information and integrated payment systems. Key features for each initiative and their anticipated impacts on traffic, the environment, and mobility patterns are described.
Traffic congestion has become one of the largest annoyances to countries like the United States and China when it comes to economic competitiveness, livability, safety, and long-term environmental sustainability. Congestion on freeways and roads is a major component of and contributor to delay. Emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), and connected vehicles provide opportunities to mitigate traffic congestion.
As one of the largest technology providers in China, Asiainfo Data is leading a city-scale Smart Transportation project in City of Wuxi, one of the national centers for IoT innovation in China. In this talk, we would like to share our real-world experiences in using AI and big data technologies to implement cutting-edge technologies transformation of Wuxi’s transportation systems. In this project, we collaborate with China Telecom, the Traffic Management Research Institute, smart vehicle makers, and many influential researchers on traffic management worldwide. We will showcase scenarios on public transportation prioritization, car-road coordination, and region-wide traffic signal optimization by utilizing AI modeling and the most advanced IoT technologies.
Speaker
Jian Chang, AsiaInfo, Chief Technology Officer
Transport for Cairo is an ambitious project that seeks to map all formal and informal transportation in Cairo digitally, and release all data openly.
This Presentation is outdated as of April 2016.
Based on a future vision of a multi-modal, end-to-end UK mobility system please describe your view of the role of Customer Experience in achieving this vision and where experience from other industry sectors can be used to add value
Cities are facing increasing mobility problems as populations grow. Public transportation systems generate large amounts of data from various sources, but there is a gap between the available data and the knowledge that can be extracted. The document discusses challenges around data integration, collaboration, and knowledge extraction in order to improve public transportation planning, operations, and passenger information systems through solutions like optimization algorithms, real-time tracking and alerts, and multimodal route planners. Political commitment is needed to fully leverage the available data.
The role of open data in driving sustainable mobility in nine smart citiesPiyush Yadav
The work was presented in European Conference on Information Systems (ECIS 2017) , at Guimaraes Portugal. The work presents a comprehensive survey results on open data focused in mobility domain in nine smart cities like Barcelona, Dublin, NewYork etc.
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Km4City: how to make smart and resilient your city, beginner documentPaolo Nesi
The Km4City platform aggregates data from multiple city domains to provide integrated data services and generate suggestions to engage citizens and support social innovation. It enables a wide range of applications while keeping city services and status under control through a flexible dashboard. The platform assesses and improves city resilience, safety and security by analyzing city usage at multiple levels. It accelerates the implementation of business and service applications by enabling integrated city services through third party portals.
Innovative Approaches for Smart City Development
ดิจิทัลไทยแลนด์ 2016: วิธีการใหม่ การพัฒนาเมืองอัจฉิริยา Trends and case studies from Germany, UK, and rest of Europe. Focus on how to get started and medium sized cities. Presented at Digital Thailand Days on 27 May 2016. www.facebook.com/events/1088455231202211
www.facebook.com/digitalthailandday/
www.digitalthailand.in.th/ #digitalthailand #digitalthailand2016
국내 중소기업,벤처기업, 스타트업의 해외 진출 영업 및 마케팅,해외 기업과 공동연구, 글로벌 VC로부터 투자유치에 대한 프로젝트 세미나중 해외 기업과 공동연구에 관하여 이스라엘의 해외 기업 공동연구 부분에 대한 발표내용만 정리했습니다. 해외진출 마케팅과 글로벌 VC로부터 투자유치에 대한 내용은 프로젝트 기밀사항이라 제외합니다.
This panel discussion at SXSW 2014 will focus on how big data and sensors are transforming transportation systems in cities. As urban populations grow dramatically, transportation infrastructure must adapt to become more efficient and sustainable. New modes of transportation enabled by data sharing and social media have the potential to revolutionize how people and goods move within cities. However, these changes also raise issues around data ownership, privacy, updating transportation policies, and defining government's role in systems that incorporate shared private resources. The panel will discuss whether transportation is the next major industry to be disrupted by big data, challenges around data control, how policy can evolve beyond a focus on roads, and whether current models for transportation planning need revamping.
How to make the best use of massive amounts of data for urban mobility. Presented by Balaji Prabhakar at Transforming Transportation 2015.
Transforming Transportation 2015: Smart Cities for Shared Prosperity is the annual conference co-organized by the World Resources Institute and the World Bank.
This document contains personal information such as name, contact details, education history, and military service history. It discusses image making as improving one's image through activities like exercise, fashion, skin care, and cosmetic procedures. A 5-step action plan for image making is outlined, including analyzing one's current image, setting an image goal, developing an image making plan, implementing the plan, and evaluating results.
Smart Cities and Big Data - Research Presentationannegalang
Research presentation on smart cities (sensor technology) and big data, presented in a graduate course I took on Transmedia Design and Digital Culture.
The document summarizes Kiichiro Hatoyama's presentation on effective countermeasures to reduce traffic congestion in metropolises like Moscow and Tokyo. Hatoyama discusses short-term measures like improving traffic regulation and infrastructure, and long-term measures like conducting comprehensive surveys, clarifying the role of public transportation, implementing intelligent transportation systems, and modifying drivers' behaviors through mobility management programs. The key is taking a holistic, data-driven approach through continued planning, implementation, evaluation, and improvement of countermeasures tailored to each individual city.
The document is an inception report from the Transport Department of the Capital city that discusses the current state of public transportation in Ulaanbaatar, Mongolia. It notes that the number of routes and vehicles have increased in recent years but problems remain. Main problems include traffic congestion, lack of infrastructure investment, and high accident rates. The report outlines plans to improve the system through introducing bus rapid transit, developing an intelligent transportation system, and conducting a feasibility study for a metro system. The long-term plan involves phased improvements over the next 30 years to modernize the fleet and convert to more sustainable fuels.
Data driven public_transportation_operation_by_trips_jaehong_minJaehong MIN
This document summarizes the TRIPS (Travel Record based Integrated Public transport operation System) developed by KRRI (Korea Railroad Research Institute) to analyze and improve public transportation systems using smart card transaction data. The key points are:
1. TRIPS uses smart card data from public transit systems to analyze current demand and usage patterns, estimate demand under different operational scenarios, and evaluate the effects of service changes.
2. It has been applied to the public transit system in Seoul, analyzing over 12 million daily transactions to examine routes, ridership, transfers and more.
3. TRIPS has also been implemented in other local governments and can help optimize local transit networks and scheduling. Further development includes integrating
Professor Amal Kumarage, Endeavour Executive Fellow, presented his research on Transport Planning as part of the SMART Seminar Series on Tuesday, 25th November 2014.
The Development of Public Transportation Strategic Plan for Metro Cebu Volume...Emmanuel Mongaya
This document provides an executive summary of the final report on developing a public transportation strategic plan for Metro Cebu. It summarizes the methodology, existing public transport situation, identification of potential medium to high capacity transit corridors, selection of transit corridors, and key findings and recommendations. The study involved comprehensive data collection and transport modeling to analyze passenger demand and identify the most suitable transit backbone corridors to form the structure of an improved public transportation system for Metro Cebu. Seven alternative transit corridors were proposed and evaluated based on passenger volume forecasts to select the priority corridors for implementation.
The document describes a proposed smart traffic management system that uses sensors and analytics to optimize traffic flow. It discusses three key aspects:
1. Sensors like ultrasonic transceivers would detect vehicle density on roads and lanes and feed this data to a controller. The controller would dynamically adjust traffic light timings based on real-time traffic conditions to reduce congestion.
2. A simulation model represents the physical traffic system mathematically to test conditions virtually before implementation.
3. Future enhancements could include diverting motorists to less crowded routes based on traffic analytics, changing all traffic lights on emergency routes to green to help ambulances reach hospitals faster, and using video and other sensor data to help prevent and solve
Shared Mobility Service for New Town DevelopmentJunyoung Choi
This document discusses plans for shared mobility services in Unjeong New Town in Paju, Korea. It begins with background on shared mobility and discusses the regional transportation challenges in Unjeong New Town. It then proposes a last mile mobility service within Unjeong using shared shuttles, bikes, scooters and ridesharing. The plan is to introduce this as a district-level transportation system to minimize commute times to Seoul. It analyzes travel patterns and proposes locating shared mobility hubs near the GTX station. Finally, it discusses future plans including using curbs as shared mobility hubs and participating in a global project on electric and shared vehicles.
The document discusses a study on the impact of the Lucknow Metro Rail project on the city's transportation system. It provides background on the growing traffic issues in Lucknow due to rising vehicle ownership. The metro project aims to provide a mass rapid transit system that is convenient, safe, quick, cost-effective and environmentally friendly. The study surveyed commuters at metro stations to understand perceptions of how the metro will affect the city's transportation. Initial results showed metro ridership had significantly decreased since launch, suggesting the metro is currently unprofitable. The research aims to understand reasons for the metro's lack of effectiveness and usage through analyzing commuter attitudes.
Building smart green mobility in South Tyrol through an open data hubSpeck&Tech
ABSTRACT: For decades the traditional approach for solving mobility and transportation challenges has been based on the idea of creating new road or rail infrastructures. Thanks to the impressive enhancement of intelligent transportation systems (ITS) technologies, in the last years this approach is going into the direction of rather improving the efficiency of how available transportation infrastructure is used. New digital infrastructures allow all mobility actors (vehicles, pedestrians, sensors, traffic management centers) to cooperate together to achieve the ambitious goal of improving mobility, enhancing safety, reducing congestion and environmental impacts. But how can we achieve this and ensure that public and private actors efficiently work together? In South Tyrol we have tried to give an answer to these challenges through the implementation of an open data hub, which enables the real-time data / information exchange among all interested parties and fosters the multiplication of development of research & innovation projects between local companies, research centers and public organizations. After years of implementation, the Open Data Hub South Tyrol is now creating the premises for a new historical phase for mobility in the region, with concepts like Mobility-as-a-Service or environmental traffic management that are finally moving from research to deployment.
BIO: Roberto Cavaliere is an ITS Project Manager at NOI Techpark Südtirol / Alto Adige, a public-owned organization in the Italian alpine region of South Tyrol coordinating the NOI Tech Park and with the mission to drive and foster research & innovation in the region. Roberto is the reference person in NOI for all initiatives in the field of ITS and smart mobility and in the last 10 years has coordinated a relevant number of EU-funded projects in this field. His main interests cover cooperative systems, autonomous driving, ITS for the environment, mobility-as-a-service and sharing mobility, road weather information systems (RWIS).
The Bournemouth University Project Management Day, an event that marks the multi-year collaboration between the Bournemouth University and the PMI UK Chapte.
1) The document discusses global trends like urbanization that are contributing to increased urban congestion and presents potential solutions like road space rationing and congestion pricing.
2) It evaluates these approaches and finds that while road space rationing addresses congestion, it is not a long term solution, whereas congestion pricing in Singapore has significantly reduced travel times and increased road safety, but requires costly infrastructure investment.
3) The document concludes that as technologies advance, governments must engage citizens to ensure accountability and transparency, and that developing countries should initially focus on improving public transportation rather than advanced technologies.
How can we make traffic flow better so fewer of us are sitting in traffic jams for shorter periods of time – if at all?
Researcher Lina Kattan looks at Intelligent Traffic Systems that optimize the operation, safety and costs of a city’s transportation network through sustainable traffic control and transportation management strategies. These systems are designed to manage traffic congestion, signal controls and prediction of bus and LRT arrivals.
Read on to learn about solutions that are working and how new developments will change the traffic jigsaw in the not-to-distant future.
You can also see the full webinar recording at: http://www.ucalgary.ca/explore/can-we-make-traffic-jams-obsolete
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Improvements in information technology related technologies are encouraging and enabling greater use of public transportation and they are enabling new forms of transportation systems that have lower carbon emissions and use less resources. Improvements in information-related technologies such as mobile phones and GPS encourage greater use of public buses, bicycle sharing systems, and trains. These same improvements are making autonomous vehicles economically feasible and roads dedicated to them. Roads dedicated to them can reduce congestion, increase fuel efficiency, and reduce accidents and costs related to them. In combination with public transportation, autonomous vehicles can reduce the need for private vehicles and thus parking spaces. Similar types of improvements in power electronics are reducing the cost and improving the performance of charging stations and thus enable more rapid recharging with a denser number of charging stations. This rapid and more frequent recharging can overcome the existing bottleneck of lower battery storage densities and slow improvements in these storage densities. Overall, improvements in information technology are making possible new forms of sustainable systems that have a much higher chance of becoming economically feasible than more commonly discussed solutions such as hybrid vehicles and wind turbines.
The document summarizes São Paulo's efforts to integrate its transit system across the large metropolitan region. Key elements included building dedicated bus rapid transit lines, implementing bus priority lanes, integrating fares, and renovating the bus fleet. This resulted in increased ridership and a shift towards greater public transit use. However, challenges remain regarding reliability, capacity, and user perceptions of service quality. Ongoing improvements continue to address these issues.
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Transportation service innovation through big data
1. 0 / 33
Kim, Ki-Byoung
Transportation Service Innovation through Big Data
- Best Practices in Seoul Metropolitan Gov. -
Apr. 28. 2016.
Chief Data Officer / Director
Data & Statistics Division
Seoul Metropolitan Government
e-mail: pskbkim@gmail.com
2. 1 / 33
Contents
1. Introduction
2. e-Government of SMG – Foundation of Seoul eGov.
3. Data to communication – Open data initiative
4. Communication to collaboration – Bukchon IoT Living lab
5. Collaboration to innovation – Data based public services
6. What Seoul has learned
7. Plan & direction
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More than 90% of Seoul citizens are
Smart Phone users
Ranked 1st on
e-Governance survey
By Rutgers Univ, NJ, USA
For 6 times since 2003
Concentration of ICT Resources
Population: 10,370,000 (2015)
20% of Korean population
Total Area: 605.26km2 GRDP : USD 319 billion(2015)
25% of Korean GDP
Seoul - Outlook
4. 3 / 33
25 districts
605 km2
490 ICT systems
- USD 25 billion dollars
- 46,500 city officials
(including fire fighters
and district officials)
Seoul Metropolitan Government
6. 5 / 33
Background - Open data initiative
Open information1
- Open documents
- Open data
- Integration & visualization
-Real time bus operational information
-Real time metro information
-Taxi vacancy information by big data
-Fine-dust, Tax revenue, expenditure, .. more than 4,000 data set
7. 6 / 33
Listen to voices of the citizen – even it’s small…
1
- Open documents
- Open data
- Integration & visualization
Listen to the citizen2
-m Voting, mobile complaint app
-“120” call center
-Big data analysis
Open information
64,226,068 calls
since 2007
as of the end of 2014 as of the end of 2013 as of the end of 2013
25.5%? 73.5%?
8. 7 / 33
Find insight of voices even it’s small, then response!
1
- Open documents
- Open data
- Integration & visualization
2
3 Listen again by data & Do
-Late night bus routes
-Taxi analysis
-Reduction of car accidents
Listen to the citizen
Open information
-m Voting, mobile complaint app
-“120” call center
-Big data analysis
10. 9 / 33
Demand driven communication based on Citizen’s needs
Data collaboration with citizen
ConnecttoopenAPI(realtimedata)
11. 10 / 33
Participation for prepare new policy via mVoting
m-Voting for listening the citizen toward new policies
12. 11 / 33
Voting through smart phones and PCs
Selected projects are planned according to
the m-Voting results
- Participation ratio
- Citizens(m-Voting): 45%
- Citizen participatory budget committee: 45%
- Survey: 10%
Citizen Participatory Budget Project (July 16th~25th, ‘15)
Citizen’s participation on the selection of city projects
Seoul citizens can decide where the $50
million city budget will be spent in 2016
Everybody can participate in m-voting
m-Voting for listening the citizen toward new policies
13. 12 / 33
Living lab for Internet of Things @ Bukchon
Living lab = Innovation eco-system where users can participate proactively
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Protect Minorities
Smart Parking
위급상황
전송
이상징후
감지 온도
동작
음성
이상징후
전송
LTE라우
터
비상벨
데이터 분석
위급상황 판단
관제서버
담당복지사
전송
담당복지사복지대상자
주차인식센서
싱크노드
무선메쉬
WiFi
Zigbee
주차인식센서
싱크노드
무선메쉬
WiFi
Zigbee
▶Safety, welfare, transportation, environments
Smart Trashbox
15. 14 / 33
Smart Street Lamp Smart meter
Smart Parcel Box
SMS, 앱
전송
수령자
핸드폰번호
입력
택배기사 택배함 중앙시스템
WiFi
비밀번호설정
수령자
[문자내용]
A-123번
보관함에
택배가 보관.
비밀번호 1913
인체감지
환경정보수집
25% 50% 100% 50%
북촌 IoT유무선
네트워크
6LoWPAN
조명색 조절
6LoWPAN
가로등 중앙관제
(고장,감시,제어)
계량기
전송기
계량기
계량기
전송기
소출력
무선통신
계량기
전송기
집중기 LTE라우터
u-Service Net
중앙시스템
고지서송부 주민
사용량 분석
▶ Safety, welfare, transportation, environments
19. 18 / 33
Night bus route optimization
‘13.06~
‘13.07
Location analysis of
Life double cropping center
‘13.10~
’14.02
Location analysis of
Senior welfare center
‘13.10~
’14.02
City PR booth analysis
‘13.10~
’14.02
Flow analysis of foreign tourists
‘13.10~
’14.02
Location analysis for ATM of civil service
‘14.11~
’15.02
Taxi analysis
‘14.07~
‘15.02
Traffic accident analysis for minorities
‘14.07~
‘15.03
Disabled taxi analysis
‘14.07~
‘15.07
Street business zone analysis
‘14.10~
‘15.10
Location analysis for public WIFI
‘15.04~
‘15.07
Analysis of regional festivals
‘15.10~
‘16.05
Mobility analysis of disabled
‘15.10~
‘16.05
Tuberculosis analysis
‘15.10~
‘16.05
Village bus route optimization
‘15.10~
‘16.05
Effectiveness of traffic signs
‘15.10~
‘16.05
Analysis of traffic accident zones
‘15.10~
‘16.05
Parking analysis
‘15.10~
‘16.05
‘ Analysis of Shinchon water fest.
‘15.10~
‘16.05
Big data based administration
1
1
1
1
1
1
1
1
1
2
2
2
2
2
3
4
3
3
3
20. 19 / 33
Area Topic Period Objectives Results
Transporta
tion
Night bus route
optimization
`13.1H Route optimization
Night bus coverage > 42% with 30
buses (daytime buses >7,000)
Transporta
tion
Traffic accident
analysis for minorities
`14.2H
Prepare policies to reduce
traffic accidents of children and
senior citizen
Number of accidents will become
50% in 3 years
Transporta
tion
Taxi analysis `14.2H
Provide more chance to catch
a taxi during mid night
Provide 5% more chance to catch a
taxi in midnight
Welfare
Location analysis of
Life double cropping
center
`14.1H
Select the best location of life
double-cropping center
Every facility will provide best
coverage for the citizen
Welfare
Location analysis of
Senior welfare center
`14.1H
Select the best location of
senior leisure centers
Every facility will provide best
coverage for the citizen
Welfare Disabled taxi analysis `14.2H
Reduce waiting time of
disabled taxi
Reduce waiting time by 10%
Admin City PR booth analysis `14.1H
Select best location of public
PR
More PR’s with same booths
Admin
Location analysis for
ATM of civil service
`14.2H Select prioritized ATM locations
Provide more ATM services with
planned number of devices
Tourism
Flow analysis of
foreign tourists
`14.1H
Promote foreign tourism with
better service
Increase foreign tourist by 10%
Results from big data based administration
21. 20 / 33
Response of the City
No public transportation
in 01:00 AM ~ 05:00 AM
Subway Bus Taxi
“Buses don’t run by the
time I get off work. I
don’t have a car.
I hope there will be
buses available at late
night..!!”
@gu****
Late-night bus story
Let’s set-up Late night bus routes
Facing Problems
1.Limited resources
– bus, drivers, budget
2. Where is traffic demand?
Why Late-night bus?
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Approach – night bus route optimization
Big data problem definition Modeling Analysis
Night bus routes Routes optimization Finalized routes
Original problem(big problem) Big data problem(small, manageable problem)
Set-up mid-night buses with limited resources Floating population and moving directions of 1,252 cells
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Results
•Citizen’s evaluation
A new way to go home at mid-night
8.9% reduction of refusal rate to
passengers of mid-night taxi
11.8% increase of women’s
mid-night activities
•Administration perspective
Proof of administration decision
to settle civil complaints
Max. 10% of PAX increased without
increasing new routes or buses
42% coverage of citizens
PassengersWanderer due to refusal of taxi Women returning home late
Daily PAX
Student BusinessmanProxy driver
8.9%▽ of refusal passengers 11.8% △ of midnight women’s activity
Source: News jelly
24. 23 / 33
Subsidy $150M / year
Why Taxi Matchmaking? Response of the City
Taxi big data analysis
•According to 120 Seoul Dasan call center
• - 25.5% of the citizens’ complaints are on
transportation!
- Among them, 73.5% are related to taxis!
Provide more supplies of taxis,
without additional no. of taxis
- Taxi DTG (Digital Tacho-graph)
-X,Y coordinate, height, date, heading, speed, status per 10 secs
- Data are collected in every 150 seconds
Private
Taxis
49,424
Corporate
Taxis
22,801
Status of taxi registered in Seoul
It seems to be short supply of taxi
during 11PM to 1AM
while Taxis in Seoul are oversupplied
Facing Problems
25. 24 / 33
Approach – taxi analysis
Big data problem definition Modeling Analysis
Vacancy vs. Demand Preparation of policy Reinforcing eco-system
Original problem(big problem) Big data problem(small, manageable problem)
More taxi supply without increasing no. of taxis Decrease vacancy rate of taxis
No vacant
taxis during
23-01hr
Vacant rate is
HIGH!
Instead of providing more taxis,
what about
reduction of vacancy rate?
Vacant rate is HIGH!Vacant rate is HIGH!
Refined node/link
Refined link length : 150m
-2 min. walking distance
-Can count taxi with speed of 60km/h
High vacancy
High demand
1
3
2
26. 25 / 33
Occupied
Transfer
21.2L Saving
1.5L
Empty
Transfer
13.9L
Reduction of
1.5 liter/day
1ML 1ML 1ML 1ML 1ML 1ML 1ML
1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML
1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML
27,000,000 liter / year
(7M gallon / year)
* 44,932 ton / year
CO2
* Applied IPCC formula for tCO2 conversion
Expected results
• Provide more chance to catch a cab by reducing empty rate of taxi
• We will reduce empty rate of taxi by 10% from now……
- Average empty rate of taxi in Seoul = 42% (Korea Transport Institute , 2012)
5% more chance
to catch a taxi
$40M annual
cost savings
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27. 26 / 33
Traffic accident analysis for children & seniors
Why traffic accidents? Response of the city
Death
Accident
Bodily Injury
Car Injury
Prededing
Clause
1
Assailant
2
Road
3
Vehicle
4
Others
5
Victim
Facilities
structure
Lane
Change Sharp
Curve
Conjuction
Management
Problem
No
Facility
Facilities
Road
Structure
Events
Bad
Weather
Night
Perception
Ability
Carelessness
Safety
Utility
Drinking
Inattention Intentional
Overspeed
- Heinrich’s law (1:29:300)
Facing problems
1.Reduce accident rate of children safety
zone and senior citizen zone
2.Where are traffic accidents of children
and senior citizens?
Assumption
1.0 1.4 1.6
2.4
4.8
코펜하겐 베를린 도쿄 런던 서울
<’2009, person/100k person>
2-4 times more traffic accident than
global cities
Walk to death accident among fatal accidents=57%
Senior accident among walk accident=38% (#1)
Protect children & senior citizen from the
traffic by reducing no. of accidents by half
28. 27 / 33
Traffic accident analysis - approaches
Problem definition Modeling Analysis (EDA)
Hot spot analysis Assumption & Proof Preparation of policy
29. 28 / 33
More cases…
1.Blind citizen’s facility operation
2.Location selection of braille blocks
3.Effectiveness analysis of traffic signs
4.Festival analysis in Seoul
5.Tuberculosis analysis
6.Route recommendation for midnight village buses
7.Traffic accident analysis
8.Parking analysis
9.Shuttle services for minorities
10.Relocation of Taxi stops
31. 30 / 33
1.Problem definition is important!
- Try to find problems by analysis? Good question makes good analysis
2. Objective of analysis?
- Quest for pearl in a grain of sand? Analysis without objectives may lead wrong direction
3. Administration problems into data problems
- Much better to analysis data based transformed problem
4. How to model data?
- Analysis raw data? Better to analysis with simplified but problem oriented model/data
5. Focused only on big data analysis?
- Good insights are sometimes coming from traditional data analysis.as well as big data
6. Apply analyzed results
- Provide analyzed results to proper departments. They will demonstrate results, not by you
What we’ve learned
*Bus route problem
-> floating population & direction problem
*Floating population
-> population of hexagons with 500 m radius
-> 605km2 of Seoul -> 1,252 cells
32. 31 / 33
1. Preparation of data
2. Sharing data/
analysis results
Big Data Campus
Integrated
Analysis
How to start big data innovation in major cities?
Transportation
Administration
-Cell phone data
-Sensors, IoT
-Community mapping 3. Private/Public
collaboration
Floating
population
34. 33 / 33
What are we going to do with data?
Transport/Safety1
Welfare2
3 Small Business
4 Environment
-Reduction of car accidents
-Optimization of local bus routes
-Automatic allocation of disabled taxi
-Optimization of civil service kiosks
-Mobility service for the disabled
-Big data for small business develop.
-Analysis for Seoul city festival
-Tuberculosis analysis
and more…
2015 ~ 2017
37. Problem definition & transformation to data problems
1.Where are the passengers
in the mid-night?
2. Where do they want to go?
Data problemsAdmin. Problems
3 billion mobile call data
45. Problem definition & transformation to data problems
1.Provide more taxis without
increasing no. of taxis
2. Increase utilization of taxis
Data problemsAdmin. Problems
Vacant rate =
𝑽𝒂𝒄𝒂𝒏𝒕 𝒓𝒖𝒏 (𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
𝑻𝒐𝒕𝒂𝒍 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
Hired rate =
𝑯𝒊𝒓𝒆𝒅 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
𝑻𝒐𝒕𝒂𝒍 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
46. 1
2
3
4
Get on Get off
Vacant
Hired
Status of taxi Get on Get off Vacant run Hired run
Hired run 1
Vacant run 1
Hired run get off
Vacant run
1 1 1
Vacant run get off
Hired run
1 1 1
1
2
3
4
Modeling – Status of taxi
47. Modeling – Seoul metropolitan area
Original
Standard node/link
Refined
Refined node/link
Average link length: 330m,
Longest link length: 30km
Refined link length : 150m
-2 min. walking distance
-Can count taxi with speed of 60km/h
-(move 150m in every 10 sec.)
49. What did big data find?
운행
대수
기준 : 4시~3시
운행
대수
50. Location Date Begin End period Vacant taxi Estimation Real vacancy rate
1. Hotel Seogyo 2014-12-11 23:00 23:30 42 89 47%
2. Seonghwa bd. 2014-12-11 23:50 00:20 28 37 76%
3. Hotel Yaja 2014-12-11 00:30 01:00 47 55 85%
승객대비 공차
많은 위치
승차 집중 위치
1
3
2
Implications
Example: Hongik Univ: Seogyo Hotel – High demand vs. Donggyoro – low demand
23 / 42
51. Suggestion - Seoul taxi map
14
15
19
홍대
입구
종각
역
동대
문역
건대
입구
가로
수길
강남
역
로데
오거
52. Accelerate eco-system of big data
Information of
Boarding and
Departing Time,
Location, and
Traveling Course
Weather
Information
Information on the
Floating Population
Node-Link
SMG,Taxis utilize shared big data
25 / 42
53. Problem definition & transformation to data problems
1.Reduce traffic accidents
of senior citizen & children
2. Reduce traffic accidents
of safety zones
Data problemsAdmin. Problems
1. Accident frequencies along with
cell model
2. Driving behavior on node/link model
3. Forecast index of accidents from
- Floating population
- Location of metro station
- Location of Crosswalk
- Location of junction
- Location of Bus stop in the middle
55. Exploratory Data Analysis (탐색적 자료 분석)
Time series analysis
Characteristics External
causes
Geo-spatial analysis
• 사고유형
• 가해자/피해자
• 연령/성별
• 차종 등
기상
유동
인구
DTG
교통
속도
도로
특성
시설물
EDA
(Exploratory Data Analysis, 탐색적 자료 분석)
• Finding characteristics of traffic accidents from external causes
• Finding hot spot of traffic accidents
• Forecast future traffic accidents along with time/location through time series
analysis and geo-spatial analysis
다차원
상관분석
56. Children’s traffic accidents
[어린이/중고생 보행자 교통사고 - 시간대별]
[성인]
[어린이]
[중/고생]
• 어린이 교통사고는 등/하교시간에
집중적으로 발생되며 등교보다는
하교시간에 더 많이 발생됨
[어린이 보행자 교통사고 - 연령별]
• 초등학교 1학년인 만 7세에 사고가 급증하며,
7~9세의 비중이 34.4%로 이 연령층에
대해서는 별도의 대책이 필요함
57. Senior citizen’s traffic accidents
• 노인 보행자 사고는 오전 4시에 급증한 후
출근시간을 지나 오전 10시경에 다시 급증하는
형태가 나타나고, 저녁 7시에 급감
구성비
Senior vs. non Senior pedestrian – Injuries
• 비노인 중상이상 비율은 42.3%인
반면, 노인 보행자는 65.4%의
중상이상 피해가 발생
• 일단 보행자 사고가 발생할 경우
노인이 더 심각한 피해를 입는다고
할 수 있음
구성비
[노인/비노인 보행자 교통사고 – 시간대별]
58. Forecast model of traffic accidents by location/time
Floating
population
Metro
Station
Cross
Walk
Junctions
Bus stops
Frequencies of
pedestrian accidents
Based on initial analysis, traffic accidents
in Seong-buk district(one of 25 districts in Seoul)
are highly correlated with floating population,
distance from metro station entrance, cross-
walk, junctions, and distance from bus stops.
Forecast model of
pedestrian traffic accidents
59. Hot spot analysis of children/senior citizen’s traffic
accidents
[어린이 보행자 교통사고 온도지도]
[노인 보행자 교통사고 온도지도]
[일반 보행자 교통사고 온도지도]
VS
60. Findings on children’s traffic accidents
과속방지턱
어린이 보행자 교통사고
[A초등학교 인근 어린이 보행자 사고 및 과속방지시설 분포]
Assumption
A1. Traffic accidents are outside of safety zones
A2. Over speed prevention facilities reduce
traffic accidents
Results
• 초등학교 정문으로부터 반경 300m이내는 모두
어린이 보호구역으로 지정되어 있고
과속방지시설이 대부분 많이 설치되어 있으나,
• 어린이 보행자 사고가 많은 초등학교 인근을
보면 과속방지시설이 전혀 설치되어 있지 않은
곳들을 발견할 수 있으며,
• 이들 지역에 대한 집중적인 과속방지시설
설치가 필요함
61. Findings on senior citizen’s traffic accidents
불광시장
경동시장
신림6동시장
신신림시장
청량리도매시장
월곡시장
[불광역 인근 – 노인 보행자 사고] [신신림시장 인근 – 노인 보행자 사고]
[청량리역 인근 – 노인 보행자 사고] [월곡역 인근 – 노인 보행자 사고]
Assumption
A1 Traffic accidents are
outside of safety zone for
senior citizens
A2. Traffic accidents are
occurred near the senior
citizen facilities
Results
• 현행 노인보호구역은
노인정, 노인복지관,
요양원 중심으로
천편일률적으로 지정
• 전통시장이나 공원 등
노인 사고 다발지역
중심으로 노인보호구역
지정이 필요함
제일시장
[일반] [일반]
[일반] [일반]
62. [무단횡단금지시설]
New facilities to prevent cross the roads
New facilities to prevent cross the road in traditional market, park
Voice guided facilities at hot spots
Relocation of senior citizen safety zones (escalation to central gov.)
[보행신호 음성안내 보조장치]
Training, promotion center (Mar. 2015~)
- Deliver safety guidelines at the senior citizen facilities
Promotions for senior citizen
Preparation of policies – senior citizen
63. Preparation of policies - children
Campaign & training for lower grade children
Customized contents, video clips, class tools (수업교구)
(2014.10~2015.6)
All year campaign →Focused campaign in Mar –Apr.
[영상컨텐츠(안)]
과속방지턱 신규설치 Facilities to prevent over speed
Traffic accident hot spot, new spots outside of safety zones
Relocation of traffic signs or safety facilities along with hot spots
Expand best practices of selected schools
Promote schools with best practices
Share best practices with other schools
64. • 성북구는 25개 자치구 중 10대 이하 어린이/청소년과 60대
이상 노년층에서 높은 보행자 사고 발생률을 보임
순위 성북구 사고 다발 지점 연간 사상자 수
1 성신여대 입구역 인근 56
2 성북구 보건소 인근 27
3 길음역 인근 - 7번출구 앞 24
4 성북성심의원 인근 19
5 길음2동 주민센터 인근 19
6 진각종 인근 오거리 17
7 종암동 주민센터 인근 16
8 길음역 인근 – 버스중앙차로 15
9 서울 장위초등학교 인근 15
10 장위로 인근 14
[성북구 사고다발지점 위치]
성북구는 다른 구에 비해 어린이/청소년/노인의
보행자 사고 비중이 높음
빅데이터 기반의 교통사고 예측모형 개발
• 유동인구, 날씨 등의 빅데이터와
횡단보도 등 안전시설물과 도로,
지하철 입구와 같은 교통시설물
등의 독립변수를 바탕으로
• 보행자 교통사고 예측모델 개발
• 이를 통해 잠재 교통사고 위험
지역 파악
65. 64 / 33
Seoul has been ranked as #1 for 6 times by Global
e-Governance Survey by Rutgers Univ. (NJ, USA)
2003
2005
2007
2009
2011-12
2013-14
e-Governance survey of Rutgers University includes
- Digital government(delivery of public services)
- Digital democracy(citizen participation in governance)
What we achieved and major considerations…
Citizen & Society Engagement
66. 65 / 33
Big Data Collaboration Campus
Big Data Analysis Platform`Big Data Cloud
B.D. Collaboration Lab. of Seoul Metropolitan Government
Citizen Research
Institute
Start-up
복제
Virtualization
Partners
융합
분석
Trans.
16
Facilities 10
Pop. 12
Company 6
Spending
6
Env. 4
Complaint 2
R. est. 4
Loc. 2
Income 1
Big Data Base Closed collaboration space
Editor's Notes
0:00 – 0;30
Hi everyone, First of all, thank you for inviting me here today.
Introduction –
- SMG integrates all data related roles such as big data, traditional data and statistical data together into one organization
- Set-up a new org. Data and Statistics Division
- Data based scientific administration
- Administration based on big data analysis
- Public data disclosure project for opening, sharing, and communication
- Statistical strategies and researches
IoT는 디바이스 관점에서는 UI를 대체하는 수단으로 부터,
방대한 데이터를 생성하는 센서까지 다양한 분야에 사용되고 있으며
빅데이터는 IoT의 활용기반이 된다.
Area: Seoul 605km2, Mumbai 603 km2(4,355km2), 7 self governments, 15 small councils
Population 10M vs. 13M
Why is data so important? Every city cannot destroy current infra and re-build them on it.
If current city infrastructure should be kept and need to be optimized, data becomes so important.
8:00 – 8:30
- 3 best practices of Seoul metropolitan government.
- additional on going projects
8:00 – 8:30
- 3 best practices of Seoul metropolitan government.
- additional on going projects
2014년 하반기, 서울시 데이터를 활용한 다양한 시도 추진
- 서울시 데이터를 시각디자인 – 랜덤웤스의 민세희 대표
- neuro associates에서 서울시 제정정보를 시민들이 직관적으로 볼 수 있게 디자dls
지하철 app, Bus app, taxi app 등
Many ICT analysis experts mentions 2015 will become a year of IoT
What about government side?
- There’s no experiences nor cases of new technology.
- So, we cannot prepare plan until a good case will be unveiled by others
- From the economy perspective, leading position will be not mine but yours!
Seoul MG made a strategic decision to establish IoT zone in Bukchon near the Seoul city hall
8:00 – 8:30
- 3 best practices of Seoul metropolitan government.
- additional on going projects
올빼미버스에서 현재까지 19건의 빅데이터 프로젝트를 진행함
Transportation/safety = 9 , welfare = 5, Small business = 4, and, Health/environment = 1 , Total 19 projects has been doing or finished at this moment.
13:00 – 15:00
There’s 9 Late-night bus route in Seoul,
we call it Owl bus. Owl bus started running since 2013.
I’d like to explain it from the perspective of communication and participation
At the beginning, owl bus discussion started from one small twitter message from a university student.
Yes, it’s stated from the very small communication with citizen. It’s small but, Seoul didn’t miss
the value of this small voice.
Now the night bus task had been set-up
But, still there were lot more problems in front of us.
-Facing problem 설명
지난 April 서울시 CIO인 Mr Kim은 올빼미 버스의 approach와 성과에 대해 소개한 바 있습니다.
오늘 저는 이를 Communication과 participation 관점에서 얘기를 해보고자 합니다.
올빼미 버스는 대표적으로 시민의 소리와 참여를 반영한 서울의 정책 사례입니다.
대학생 김**씨는 트위터를 통해 심야대중교통의 불편함을 서울시장에게 호소하였습니다.
서울시는 즉시 심야버스를 검토하였는데요.. 여러가지 해결해야 할 문제점들이 나열되었습니다.
심야라는 특성 상, 서울시 전역을 커버할 수 있는 많은 버스노선과 지하철을 운행할 수 없었습니다.
(서울은 세계에서 가장 편리한 대중교통시스템을 운영하는 도시입니다)
두번째는 사람들이 퇴근한 심야시간대에 어디에 승객들이 많이 모여있느냐는 것이죠
세번째는 이러한 승객들이 어디로 갈 것인가?라는 문제입니다.
15:00 – 20:00
Transportation problem -> Big data problem
Big data problem -> manageable model
And, analyze
Prepare - Define task & problem
Transform original problems(Big problem) into big data problem(Smaller problem)
Establish manageable model, but a huge big and complex model
Analyze goal-oriented approach. remember objective is not a big data…
20:00 – 22:00
- The results was amazing.
With same, limited budget and resources, we can create bus route with 10% more passengers.
And, only 9 routes of night bus, SMG can cover more than 42% of citizens.
At the beginning of my talk today, I mentioned Seoul opened all the data and information to the citizen.
As a results, citizen can evaluate the results of Owl bus. I’m going to show you one of the citizen’s evaluation.
7:00 – 8:00
I’d like to share another example of communication and participation in Seoul.
Recently, 621 thousand complaint call has been analyzed. After that, we find top most complaint was transportation, it’s 25%,
And, among them, 73% are related with Taxi.
Immediately, my team started analyzing taxi Digital Tachgraph data. There are 130 billion case data in a year.
15:00 – 20:00
Transportation problem -> Big data problem
Big data problem -> manageable model
And, analyze
Prepare - Define task & problem
Transform original problems(Big problem) into big data problem(Smaller problem)
Establish manageable model, but a huge big and complex model
Analyze goal-oriented approach. remember objective is not a big data…
8:30 – 9:00
Ok, now, I’d like to show you the results of taxi match making. As the problem is defined as reduction of empty transfer rate, if empty transfer rate become decreased by 10%, we can have 27 million liter gas saving in a year, and reduce 45 k CO2 emission in a year. 27 million liter = 7 million gallon… $50M cost reduction..
From the citizen’s perspective, Citizen can have 5% more chance to catch a cab with same number of taxi in Seoul.
36:00 – 37:00
11:00- 11:30
One of the most useful data is floating population in real time
or every hour or every day.
13:30- 15:00
With connected data and data administration,
Seoul city have finished 8 big data projects since 2013.
For 2015, SMG plan to do 7 projects such as …
They are Safety, Welfare, Small business development and environment.
Why 150m?
최대한 짧은 간격일수록 좋으나, 너무 짧으면 지나가는 택시가 해당 링크에 카운트안될 가능성이 높아지기 때문에 그 적정선을 150m로 선정 (150m는 도보로 이동하는데 2분 미만이 걸리고, 시속 60km(10초당 약 150m) 달리는 택시도 카운트 가능함)
Top demand spot은 당연히 찾는 것임
심야시간대의 홍대입구역에서 합정역까지의 양화로에는 빈택시는 많으나 택시 승차가 집중되어 택시잡기가 어렵지만, 서쪽 이면도로인 동교로에서는 승차횟수가 적어 비교적 빈택시 발견하기가 용이한 것으로 나타났다.
데이터 수집-분석-가공하여
열린데이터 광장에 데이터셋 개방
개방된 데이터를 활용하여,
승객- 택시잡기 좋은 곳
운전자 – 승객태우기 좋은 곳
서비스 개발
37:00 – 38:00
1:30 – 2:00
Rutgers E-governance survey
- Digital government(delivery of public services)
- Digital democracy(citizen participation in governance)
E-Government of Seoul
- Passion & Concentrated ICT resources
- Well established ICT strategies & plans
- Strong power of executions
- And, citizen – centered approaches, citizen – oriented result driven
Results
- ranked as #1 e-government city among global top 100 cities for 6 times since 2003